/*
 * Tencent is pleased to support the open source community by making Angel available.
 *
 * Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
 * compliance with the License. You may obtain a copy of the License at
 *
 * https://opensource.org/licenses/Apache-2.0
 *
 * Unless required by applicable law or agreed to in writing, software distributed under the License
 * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 * or implied. See the License for the specific language governing permissions and limitations under
 * the License.
 *
 */

package com.tencent.angel.spark.automl.feature

import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector}
import org.apache.spark.sql.{Dataset, Row}

object FeatureUtils {

  def maxDim(dataset: Dataset[Row], col: String = "features"): Int = {
    dataset.select(col).rdd.mapPartitions { rows: Iterator[Row] =>
      val dim = rows.map { case Row(v: Vector) =>
        v match {
          case sv: SparseVector => sv.indices.last
          case dv: DenseVector => dv.size
        }
      }.max
      Iterator(dim)
    }.max + 1
  }

  def countNonZero(dataset: Dataset[Row], col: String = "features"): Array[Int] = {
    dataset.select(col).rdd.mapPartitions { rows: Iterator[Row] =>
      val mergeIndices = rows.map { case Row(v: Vector) =>
        v match {
          case sv: SparseVector =>
            sv.indices.toList
        }
      }.reduce(_ union _ distinct)
      Iterator(mergeIndices)
    }.reduce((a, b) => (a union b).distinct).toArray
  }

}
